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Calinon, S. and Billard, A. (2008)
Learning Algorithms and Systems Laboratory (LASA), Ecole Polytechnique Federale de Lausanne (EPFL), IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS).

Abstract: We present a probabilistic architecture for solving generically
the problem of extracting the task constraints through a
Programming by Demonstration (PbD) framework and for generalizing
the acquired knowledge to various situations. We propose an
approach based on Gaussian Mixture Regression (GMR) to find
automatically a controller for the robot reproducing the essential
characteristics of the skill by handling simultaneously
constraints in joint space and in task space. Experiments with two
5-DOFs Katana robots are then presented with two manipulation
tasks consisting of handling and displacing a set of objects.